In order to meet the increasing demand for wireless data traffic since the commercialization of 4G communication systems, the development focus is on the 5th Generation (5G) or pre-5G communication system. For this reason, the 5G or pre-5G communication system is called a beyond 4G network communication system or post Long Term Evolution (LTE) system.
Consideration is being given to implementing the 5G communication system in millimeter wave (mmWave) frequency bands (e.g., 60 GHz bands) to accomplish higher data rates. In order to increase the propagation distance by mitigating propagation loss in the 5G communication system, discussions are underway about various techniques such as beamforming, massive MIMO, Full Dimensional MIMO (FD-MIMO), array antenna, analog beamforming, and large scale antenna.
Also, in order to enhance network performance of the 5G communication system, developments are underway of various techniques such as evolved small cell, advanced small cell, cloud Radio Access Network (cloud RAN), ultra-dense network, Device to Device (D2D) communication, wireless backhaul, moving network, cooperative communication, Coordinated Multi-Points (CoMP), and interference cancellation.
Furthermore, the ongoing research for 5G system includes the use of Hybrid FSK and QAM Modulation (FQAM) and Sliding Window Superposition Coding (SWSC) as Advanced Coding Modulation (ACM), Filter Bank Multi Carrier (FBMC), Non-Orthogonal Multiple Access (NOMA), and Sparse Code Multiple Access (SCMA).
With the advance of electronic technologies, various types of inter-device data communication methods are developed. Particularly when remote devices are communicating data through a radio channel, the data are likely to be modulated and coded. In the case of wireless data communication between electronic devices, e.g., between a portable terminal and a base station or an access point, it is typical to assume a Gaussian interference environment to operate the system with a low complexity. In order for the interference signals to show close to the Gaussian characteristics, QAM schemes have been used.
In a comparison between the Gaussian channel environment and non-Gaussian channel environment in a wireless communication, it is shown that the non-Gaussian channel has a channel capacity greater than that of the Gaussian channel. Since the non-Gaussian channel capacity is greater than the channel capacity under the assumption of the Gaussian channel, it may be possible to accomplish more network throughput on the non-Gaussian channel than that on the Gaussian channel.
With the recent requirements for advances in data rates, there is a need to develop a modulation scheme capable of modeling interference signals with non-Gaussian characteristics for throughput enhancement in a wireless communication system. As one of such modulation schemes for modeling the channel interference showing non-Gaussian characteristics, the FQAM scheme has been proposed.
In order to increase data throughput with FQAM, it is necessary to use a channel coding scheme suited to the corresponding modulation scheme. In the case of using a modulation scheme with a modulation order of q, it may be possible to secure the logical channel capacity using a Coded Modulation (CM) scheme employing a non-binary channel code with the same order; however, the non-binary channel code has a drawback in having very high complexity in comparison with a binary code.
Legacy QAM series modulation schemes can accomplish throughput close to the logical channel capacity using a Bit-Interleaved Coded Modulation (BICM) scheme with a Gray mapping. However, it is impossible to accomplish any intended throughput using the FQAM scheme along with the BICM scheme. BICM with Integrative Decoding (BICM-ID) is a modulation scheme proposed to overcome this problem in such a way of iterative decoding between a decoder and a demodulator. By applying an Irregular Repeat Partially Accumulate (IRPA) code-based BICM-ID scheme to the FQAM scheme, it is possible to achieve throughput close to the logical channel capacity.
Here, the IRPA code-based BICM-ID decoder may consist of an inner decoder and an outer decoder. A receiver performs decoding in an iterative manner such that the inner decoder and the outer decoder exchange soft message processing results repeatedly. The decoding scheme of the IRPA code-based BICM-ID decoder operating as above has a drawback in having very high complexity. For example, although the outer decoder, which performs calculation on message nodes, does not contribute to the complexity, the inner decoder has very high complexity.